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Patricia Suarez, A. D. S. (2024). A Generative Model for Guided Thermal Image Super-Resolution. In In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024.
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Henry Velesaca Lara, P. S., Darío Carpio & Angel Sappa. (2024). Fruit Grading based on Deep Learning and Active Vision System. In Accepted in CIIA – II International Conference of Applied Industrial Engineering.
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Henry Velesaca Lara, J. A. H. & J. M. G. (2024). Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA. In Accepted in CIIA – II International Conference of Applied Industrial Engineering.
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Patricia Suarez, A. S. (2024). Depth-Conditioned Thermal-like Image Generation. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Omar Coello, M. C., Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca. (2024). Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Luis Chuquimarca, B. X. V. & S. V. (2024). Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Xavier Soria, A. S., Patricio Humanante, Arash Akbarinia. (2023). Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139.
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Suarez Patricia, Carpio Dario, & Sappa Angel D. (2023). A Deep Learning Based Approach for Synthesizing Realistic Depth Maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 (Vol. 14234 LNCS, pp. 369–380).
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Steven Silva, N. V., Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez. (2023). Online Social Robot Navigation in Indoor, Large and Crowded Environments. In IEEE International Conference on Robotics and Automation (ICRA 2023) (Vol. 2023-May, pp. 9749–9756).
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Cristhian A. Aguilera, C. A., Cristóbal A. Navarro, & Angel D. Sappa. (2020). Fast CNN Stereo Depth Estimation through Embedded GPU Devices. Sensors 2020, Vol. 2020-June(11), pp. 1–13.
Abstract: Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices.
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